Deep learning and computer vision have experienced significant advancements, particularly in critical applications such as autonomous driving and real-time surveillance, where object detection (OD) plays a pivotal role. Ensuring the accuracy and speed of these systems is paramount to prevent accidents or failures. Recently, latency-based attacks have emerged as a new threat, driven by the essential need for real-time performance in various applications. These attacks target model responsiveness to disrupt system performance without necessarily compromising accuracy. Our preliminary experiments show that introducing just a few bit flips to key parameters in OD models can significantly increase latency, degrading performance. Meanwhile, recent advancements in memory-based attacks, such as Row Hammer [18], demonstrate the ability to conveniently introduce bit flips at desired locations without physical hardware interaction. Based on the observations, we propose a novel attack on OD models that leverages row-hammer to introduce bit-flips via side channels, targeting the non-maximum suppression (NMS) filter and significantly increasing latency. Unlike previous methods that modify input data, our technique ensures efficiency by minimizing bit-flips through critical path exploitation and achieves practical applicability with only a subset of validation data. Experiments across various datasets and models validate our approach, demonstrating latency increases up to 71.6 ms (20.4×) with just 31 bit-flips.

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework namedAR-Lightand test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy.

We introduce the Safe Offline-to-Online Multi-Agent Decision Transformer (SO2-MADT), an innovative framework that revolutionizes safety considerations in Multi-agent Reinforcement Learning (MARL) through a novel sequence modeling approach. Leveraging the dynamic capabilities inherent in Decision Transformers, our methodology seamlessly incorporates safety protocols as a cornerstone element, ensuring secure operations throughout both the offline pre-training phase and the adaptive online fine-tuning phase. At the core of our framework lie two pivotal innovations: the Safety-To-Go (STG) token, embedding safety at a macro level, and the Agent Prioritization Module (APM), facilitating explicit credit assignment at a micro level. Through extensive testing against the challenging environments of the StarCraft Multi-Agent Challenge (SMAC) and Multi-agent MuJoCo, our SO2-MADT not only excels in offline pre-training but also demonstrates superior performance during online fine-tuning, without any degradation in performance. The implications of our work provide a pathway for deployment in critical real-world applications where safety is paramount and non-negotiable. The code is available at https://github.com/shahaamirbader/SO2-MADT.